Breaking Silos: Lessons from Research Networking in Hawaii
When Brian transitioned from government contracting into academia, he joined the University of Hawaii at a moment when its research departments were growing but fragmented. Each lab or project had its own infrastructure, its own data storage, and its own rules. The result was predictable: duplicated work, incompatibility, and researchers who rarely shared insights with one another.
Brian’s role was to connect those silos. It was not glamorous work, but it was essential. He became the bridge between ocean science, power grid research, and computer science, making their data flow in ways that helped them see common problems and shared solutions.
The Problem of Siloed Research
Academic research tends to reward specialization. Teams become protective of their data, sometimes out of habit, sometimes because of funding requirements. Brian quickly realized that this culture led to inefficiencies. Two projects might be measuring energy usage with the same grade of sensors, yet they were storing their results in incompatible formats.
This problem is not unique to Hawaii. Studies show that data silos remain one of the largest barriers to interdisciplinary science. A 2018 Nature report on open data policies highlighted that while sharing has improved, disciplinary walls still limit collaboration.
Practical Takeaway: Standardize Data Early
One of Brian’s key efforts was convincing researchers to move away from flat comma-delimited files. His goal was to encourage adoption of big data tools that could ingest different sources and allow more flexible queries. He worked to introduce Splunk, a platform designed for log management, into the academic setting as a way to process and compare diverse inputs.
Lesson for teams today:
If you are starting a new research or IT project, agree on data standards at the beginning.
Use widely accepted formats like JSON or Parquet, which support better interoperability.
Look at platforms that handle variety natively, rather than forcing uniformity after the fact.
Field Example: Monitoring Power on Maui
One of Brian’s most memorable projects involved instrumenting neighborhoods on Maui with power quality sensors. The research team wanted to understand how rooftop solar was affecting the local grid. Sensors attached to transformers tracked how electricity flowed and where bottlenecks occurred.
The insights were clear: when too many homes pushed solar power back into the grid, transformers overheated, sometimes to the point of failure. These findings shaped policies around “stepped curtailment,” where inverters could be dialed back remotely to prevent infrastructure damage (energy.gov).
Practical Takeaway: Interoperability Builds Resilience
Brian’s real success was not just the technology but the cross-team collaboration. The data pulled from Maui’s power grid was not just useful to electrical engineers. Oceanographers studying offshore equipment reliability learned from it, as did computer scientists working on distributed systems. By making the data legible across silos, Brian multiplied its value.
Lesson for IT and research managers:
Interoperability is not optional when dealing with shared infrastructure.
Build systems with multiple disciplines in mind. Even if the initial project scope is narrow, assume future researchers or departments will need access.
Encourage a culture where sharing data is considered part of the job, not a distraction from it.
Resources on Breaking Silos
NIH Data Sharing Policies and Resources — overview of requirements and support for interdisciplinary projects.
Splunk in Education — details on academic programs and grants that support data integration.
National Renewable Energy Lab (NREL) — examples of how standardized energy data is used across multiple projects.